--- _id: '10762' abstract: - lang: eng text: Methods inspired from machine learning have recently attracted great interest in the computational study of quantum many-particle systems. So far, however, it has proven challenging to deal with microscopic models in which the total number of particles is not conserved. To address this issue, we propose a new variant of neural network states, which we term neural coherent states. Taking the Fröhlich impurity model as a case study, we show that neural coherent states can learn the ground state of non-additive systems very well. In particular, we observe substantial improvement over the standard coherent state estimates in the most challenging intermediate coupling regime. Our approach is generic and does not assume specific details of the system, suggesting wide applications. acknowledgement: "We acknowledge fruitful discussions with Giacomo Bighin, Giammarco Fabiani, Areg Ghazaryan, Christoph\r\nLampert, and Artem Volosniev at various stages of this work. W.R. is a recipient of a DOC Fellowship of the\r\nAustrian Academy of Sciences and has received funding from the EU Horizon 2020 programme under the Marie\r\nSkłodowska-Curie Grant Agreement No. 665385. M. L. acknowledges support by the European Research Council (ERC) Starting Grant No. 801770 (ANGULON). This work is part of the Shell-NWO/FOM-initiative “Computational sciences for energy research” of Shell and Chemical Sciences, Earth and Life Sciences, Physical Sciences, FOM and STW." article_processing_charge: No author: - first_name: Wojciech full_name: Rzadkowski, Wojciech id: 48C55298-F248-11E8-B48F-1D18A9856A87 last_name: Rzadkowski orcid: 0000-0002-1106-4419 - first_name: Mikhail full_name: Lemeshko, Mikhail id: 37CB05FA-F248-11E8-B48F-1D18A9856A87 last_name: Lemeshko orcid: 0000-0002-6990-7802 - first_name: Johan H. full_name: Mentink, Johan H. last_name: Mentink citation: ama: Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for non-additive systems. arXiv. doi:10.48550/arXiv.2105.15193 apa: Rzadkowski, W., Lemeshko, M., & Mentink, J. H. (n.d.). Artificial neural network states for non-additive systems. arXiv. https://doi.org/10.48550/arXiv.2105.15193 chicago: Rzadkowski, Wojciech, Mikhail Lemeshko, and Johan H. Mentink. “Artificial Neural Network States for Non-Additive Systems.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2105.15193. ieee: W. Rzadkowski, M. Lemeshko, and J. H. Mentink, “Artificial neural network states for non-additive systems,” arXiv. . ista: Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for non-additive systems. arXiv, 10.48550/arXiv.2105.15193. mla: Rzadkowski, Wojciech, et al. “Artificial Neural Network States for Non-Additive Systems.” ArXiv, doi:10.48550/arXiv.2105.15193. short: W. Rzadkowski, M. Lemeshko, J.H. Mentink, ArXiv (n.d.). date_created: 2022-02-17T11:18:57Z date_published: 2021-05-31T00:00:00Z date_updated: 2023-09-07T13:44:16Z day: '31' department: - _id: MiLe doi: 10.48550/arXiv.2105.15193 ec_funded: 1 external_id: arxiv: - '2105.15193' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2105.15193 month: '05' oa: 1 oa_version: Preprint page: '2105.15193' project: - _id: 2688CF98-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '801770' name: 'Angulon: physics and applications of a new quasiparticle' - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: arXiv publication_status: submitted related_material: record: - id: '10759' relation: dissertation_contains status: public status: public title: Artificial neural network states for non-additive systems type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '9418' abstract: - lang: eng text: "Deep learning is best known for its empirical success across a wide range of applications\r\nspanning computer vision, natural language processing and speech. Of equal significance,\r\nthough perhaps less known, are its ramifications for learning theory: deep networks have\r\nbeen observed to perform surprisingly well in the high-capacity regime, aka the overfitting\r\nor underspecified regime. Classically, this regime on the far right of the bias-variance curve\r\nis associated with poor generalisation; however, recent experiments with deep networks\r\nchallenge this view.\r\n\r\nThis thesis is devoted to investigating various aspects of underspecification in deep learning.\r\nFirst, we argue that deep learning models are underspecified on two levels: a) any given\r\ntraining dataset can be fit by many different functions, and b) any given function can be\r\nexpressed by many different parameter configurations. We refer to the second kind of\r\nunderspecification as parameterisation redundancy and we precisely characterise its extent.\r\nSecond, we characterise the implicit criteria (the inductive bias) that guide learning in the\r\nunderspecified regime. Specifically, we consider a nonlinear but tractable classification\r\nsetting, and show that given the choice, neural networks learn classifiers with a large margin.\r\nThird, we consider learning scenarios where the inductive bias is not by itself sufficient to\r\ndeal with underspecification. We then study different ways of ‘tightening the specification’: i)\r\nIn the setting of representation learning with variational autoencoders, we propose a hand-\r\ncrafted regulariser based on mutual information. ii) In the setting of binary classification, we\r\nconsider soft-label (real-valued) supervision. We derive a generalisation bound for linear\r\nnetworks supervised in this way and verify that soft labels facilitate fast learning. Finally, we\r\nexplore an application of soft-label supervision to the training of multi-exit models." acknowledged_ssus: - _id: ScienComp - _id: CampIT - _id: E-Lib alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Phuong full_name: Bui Thi Mai, Phuong id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87 last_name: Bui Thi Mai citation: ama: Phuong M. Underspecification in deep learning. 2021. doi:10.15479/AT:ISTA:9418 apa: Phuong, M. (2021). Underspecification in deep learning. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:9418 chicago: Phuong, Mary. “Underspecification in Deep Learning.” Institute of Science and Technology Austria, 2021. https://doi.org/10.15479/AT:ISTA:9418. ieee: M. Phuong, “Underspecification in deep learning,” Institute of Science and Technology Austria, 2021. ista: Phuong M. 2021. Underspecification in deep learning. Institute of Science and Technology Austria. mla: Phuong, Mary. Underspecification in Deep Learning. Institute of Science and Technology Austria, 2021, doi:10.15479/AT:ISTA:9418. short: M. Phuong, Underspecification in Deep Learning, Institute of Science and Technology Austria, 2021. date_created: 2021-05-24T13:06:23Z date_published: 2021-05-30T00:00:00Z date_updated: 2023-09-08T11:11:12Z day: '30' ddc: - '000' degree_awarded: PhD department: - _id: GradSch - _id: ChLa doi: 10.15479/AT:ISTA:9418 file: - access_level: open_access checksum: 4f0abe64114cfed264f9d36e8d1197e3 content_type: application/pdf creator: bphuong date_created: 2021-05-24T11:22:29Z date_updated: 2021-05-24T11:22:29Z file_id: '9419' file_name: mph-thesis-v519-pdfimages.pdf file_size: 2673905 relation: main_file success: 1 - access_level: closed checksum: f5699e876bc770a9b0df8345a77720a2 content_type: application/zip creator: bphuong date_created: 2021-05-24T11:56:02Z date_updated: 2021-05-24T11:56:02Z file_id: '9420' file_name: thesis.zip file_size: 92995100 relation: source_file file_date_updated: 2021-05-24T11:56:02Z has_accepted_license: '1' language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: '125' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '7435' relation: part_of_dissertation status: deleted - id: '7481' relation: part_of_dissertation status: public - id: '9416' relation: part_of_dissertation status: public - id: '7479' relation: part_of_dissertation status: public status: public supervisor: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 title: Underspecification in deep learning type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2021' ... --- _id: '14177' abstract: - lang: eng text: "The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data in a large-scale empirical study (including 4260 models). We show and quantify that systematically induced correlations in the dataset are being learned and reflected in the latent representations, which has implications for downstream applications of disentanglement such as fairness. We also demonstrate how to resolve these latent correlations, either using weak supervision during\r\ntraining or by post-hoc correcting a pre-trained model with a small number of labels." alternative_title: - PMLR article_processing_charge: No author: - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Elliot full_name: Creager, Elliot last_name: Creager - first_name: Niki full_name: Kilbertus, Niki last_name: Kilbertus - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Anirudh full_name: Goyal, Anirudh last_name: Goyal - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer citation: ama: 'Träuble F, Creager E, Kilbertus N, et al. On disentangled representations learned from correlated data. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:10401-10412.' apa: 'Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal, A., … Bauer, S. (2021). On disentangled representations learned from correlated data. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 10401–10412). Virtual: ML Research Press.' chicago: Träuble, Frederik, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer. “On Disentangled Representations Learned from Correlated Data.” In Proceedings of the 38th International Conference on Machine Learning, 139:10401–12. ML Research Press, 2021. ieee: F. Träuble et al., “On disentangled representations learned from correlated data,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 10401–10412. ista: 'Träuble F, Creager E, Kilbertus N, Locatello F, Dittadi A, Goyal A, Schölkopf B, Bauer S. 2021. On disentangled representations learned from correlated data. Proceedings of the 38th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 139, 10401–10412.' mla: Träuble, Frederik, et al. “On Disentangled Representations Learned from Correlated Data.” Proceedings of the 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 10401–12. short: F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal, B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 10401–10412. conference: end_date: 2021-07-24 location: Virtual name: 'ICML: International Conference on Machine Learning' start_date: 2021-07-18 date_created: 2023-08-22T14:03:47Z date_published: 2021-08-01T00:00:00Z date_updated: 2023-09-11T10:18:48Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '2006.07886' intvolume: ' 139' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2006.07886 month: '08' oa: 1 oa_version: Published Version page: 10401-10412 publication: Proceedings of the 38th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: On disentangled representations learned from correlated data type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 139 year: '2021' ... --- _id: '14176' abstract: - lang: eng text: "Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients. In machine learning, online monitoring is often formulated as a supervised learning problem. Recently, contrastive learning approaches have demonstrated promising improvements over competitive supervised benchmarks. These methods rely on well-understood data augmentation techniques developed for image data which do not apply to online monitoring. In this work, we overcome this limitation by\r\nsupplementing time-series data augmentation techniques with a novel contrastive\r\nlearning objective which we call neighborhood contrastive learning (NCL). Our objective explicitly groups together contiguous time segments from each patient while maintaining state-specific information. Our experiments demonstrate a marked improvement over existing work applying contrastive methods to medical time-series." alternative_title: - PMLR article_processing_charge: No author: - first_name: Hugo full_name: Yèche, Hugo last_name: Yèche - first_name: Gideon full_name: Dresdner, Gideon last_name: Dresdner - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Matthias full_name: Hüser, Matthias last_name: Hüser - first_name: Gunnar full_name: Rätsch, Gunnar last_name: Rätsch citation: ama: 'Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. Neighborhood contrastive learning applied to online patient monitoring. In: Proceedings of 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:11964-11974.' apa: 'Yèche, H., Dresdner, G., Locatello, F., Hüser, M., & Rätsch, G. (2021). Neighborhood contrastive learning applied to online patient monitoring. In Proceedings of 38th International Conference on Machine Learning (Vol. 139, pp. 11964–11974). Virtual: ML Research Press.' chicago: Yèche, Hugo, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and Gunnar Rätsch. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” In Proceedings of 38th International Conference on Machine Learning, 139:11964–74. ML Research Press, 2021. ieee: H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch, “Neighborhood contrastive learning applied to online patient monitoring,” in Proceedings of 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 11964–11974. ista: Yèche H, Dresdner G, Locatello F, Hüser M, Rätsch G. 2021. Neighborhood contrastive learning applied to online patient monitoring. Proceedings of 38th International Conference on Machine Learning. International Conference on Machine Learning, PMLR, vol. 139, 11964–11974. mla: Yèche, Hugo, et al. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” Proceedings of 38th International Conference on Machine Learning, vol. 139, ML Research Press, 2021, pp. 11964–74. short: H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings of 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 11964–11974. conference: end_date: 2021-07-24 location: Virtual name: International Conference on Machine Learning start_date: 2021-07-18 date_created: 2023-08-22T14:03:04Z date_published: 2021-08-01T00:00:00Z date_updated: 2023-09-11T10:16:55Z day: '01' department: - _id: FrLo extern: '1' external_id: arxiv: - '2106.05142' intvolume: ' 139' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2106.05142 month: '08' oa: 1 oa_version: Preprint page: 11964-11974 publication: Proceedings of 38th International Conference on Machine Learning publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: Neighborhood contrastive learning applied to online patient monitoring type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 139 year: '2021' ... --- _id: '14182' abstract: - lang: eng text: "When machine learning systems meet real world applications, accuracy is only\r\none of several requirements. In this paper, we assay a complementary\r\nperspective originating from the increasing availability of pre-trained and\r\nregularly improving state-of-the-art models. While new improved models develop\r\nat a fast pace, downstream tasks vary more slowly or stay constant. Assume that\r\nwe have a large unlabelled data set for which we want to maintain accurate\r\npredictions. Whenever a new and presumably better ML models becomes available,\r\nwe encounter two problems: (i) given a limited budget, which data points should\r\nbe re-evaluated using the new model?; and (ii) if the new predictions differ\r\nfrom the current ones, should we update? Problem (i) is about compute cost,\r\nwhich matters for very large data sets and models. Problem (ii) is about\r\nmaintaining consistency of the predictions, which can be highly relevant for\r\ndownstream applications; our demand is to avoid negative flips, i.e., changing\r\ncorrect to incorrect predictions. In this paper, we formalize the Prediction\r\nUpdate Problem and present an efficient probabilistic approach as answer to the\r\nabove questions. In extensive experiments on standard classification benchmark\r\ndata sets, we show that our method outperforms alternative strategies along key\r\nmetrics for backward-compatible prediction updates." article_processing_charge: No author: - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Julius von full_name: Kügelgen, Julius von last_name: Kügelgen - first_name: Matthäus full_name: Kleindessner, Matthäus last_name: Kleindessner - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Peter full_name: Gehler, Peter last_name: Gehler citation: ama: 'Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler P. Backward-compatible prediction updates: A probabilistic approach. In: 35th Conference on Neural Information Processing Systems. Vol 34. ; 2021:116-128.' apa: 'Träuble, F., Kügelgen, J. von, Kleindessner, M., Locatello, F., Schölkopf, B., & Gehler, P. (2021). Backward-compatible prediction updates: A probabilistic approach. In 35th Conference on Neural Information Processing Systems (Vol. 34, pp. 116–128). Virtual.' chicago: 'Träuble, Frederik, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, and Peter Gehler. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” In 35th Conference on Neural Information Processing Systems, 34:116–28, 2021.' ieee: 'F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, and P. Gehler, “Backward-compatible prediction updates: A probabilistic approach,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 116–128.' ista: 'Träuble F, Kügelgen J von, Kleindessner M, Locatello F, Schölkopf B, Gehler P. 2021. Backward-compatible prediction updates: A probabilistic approach. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 116–128.' mla: 'Träuble, Frederik, et al. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” 35th Conference on Neural Information Processing Systems, vol. 34, 2021, pp. 116–28.' short: F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021, pp. 116–128. conference: end_date: 2021-12-10 location: Virtual name: 'NeurIPS: Neural Information Processing Systems' start_date: 2021-12-07 date_created: 2023-08-22T14:05:41Z date_published: 2021-07-02T00:00:00Z date_updated: 2023-09-11T11:31:59Z day: '02' department: - _id: FrLo extern: '1' external_id: arxiv: - '2107.01057' intvolume: ' 34' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2107.01057 month: '07' oa: 1 oa_version: Preprint page: 116-128 publication: 35th Conference on Neural Information Processing Systems publication_identifier: isbn: - '9781713845393' publication_status: published quality_controlled: '1' status: public title: 'Backward-compatible prediction updates: A probabilistic approach' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 34 year: '2021' ... --- _id: '14181' abstract: - lang: eng text: Variational Inference makes a trade-off between the capacity of the variational family and the tractability of finding an approximate posterior distribution. Instead, Boosting Variational Inference allows practitioners to obtain increasingly good posterior approximations by spending more compute. The main obstacle to widespread adoption of Boosting Variational Inference is the amount of resources necessary to improve over a strong Variational Inference baseline. In our work, we trace this limitation back to the global curvature of the KL-divergence. We characterize how the global curvature impacts time and memory consumption, address the problem with the notion of local curvature, and provide a novel approximate backtracking algorithm for estimating local curvature. We give new theoretical convergence rates for our algorithms and provide experimental validation on synthetic and real-world datasets. article_processing_charge: No author: - first_name: Gideon full_name: Dresdner, Gideon last_name: Dresdner - first_name: Saurav full_name: Shekhar, Saurav last_name: Shekhar - first_name: Fabian full_name: Pedregosa, Fabian last_name: Pedregosa - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Gunnar full_name: Rätsch, Gunnar last_name: Rätsch citation: ama: 'Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. Boosting variational inference with locally adaptive step-sizes. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence; 2021:2337-2343. doi:10.24963/ijcai.2021/322' apa: 'Dresdner, G., Shekhar, S., Pedregosa, F., Locatello, F., & Rätsch, G. (2021). Boosting variational inference with locally adaptive step-sizes. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (pp. 2337–2343). Montreal, Canada: International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2021/322' chicago: Dresdner, Gideon, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, and Gunnar Rätsch. “Boosting Variational Inference with Locally Adaptive Step-Sizes.” In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2337–43. International Joint Conferences on Artificial Intelligence, 2021. https://doi.org/10.24963/ijcai.2021/322. ieee: G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, and G. Rätsch, “Boosting variational inference with locally adaptive step-sizes,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada, 2021, pp. 2337–2343. ista: 'Dresdner G, Shekhar S, Pedregosa F, Locatello F, Rätsch G. 2021. Boosting variational inference with locally adaptive step-sizes. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. IJCAI: International Joint Conference on Artificial Intelligence, 2337–2343.' mla: Dresdner, Gideon, et al. “Boosting Variational Inference with Locally Adaptive Step-Sizes.” Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–43, doi:10.24963/ijcai.2021/322. short: G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343. conference: end_date: 2021-08-27 location: Montreal, Canada name: 'IJCAI: International Joint Conference on Artificial Intelligence' start_date: 2021-08-19 date_created: 2023-08-22T14:05:14Z date_published: 2021-05-19T00:00:00Z date_updated: 2023-09-11T11:14:30Z day: '19' department: - _id: FrLo doi: 10.24963/ijcai.2021/322 extern: '1' external_id: arxiv: - '2105.09240' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2105.09240 month: '05' oa: 1 oa_version: Published Version page: 2337-2343 publication: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence publication_identifier: eisbn: - '9780999241196' publication_status: published publisher: International Joint Conferences on Artificial Intelligence quality_controlled: '1' status: public title: Boosting variational inference with locally adaptive step-sizes type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '14179' abstract: - lang: eng text: Self-supervised representation learning has shown remarkable success in a number of domains. A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant. We seek to understand the empirical success of this approach from a theoretical perspective. We formulate the augmentation process as a latent variable model by postulating a partition of the latent representation into a content component, which is assumed invariant to augmentation, and a style component, which is allowed to change. Unlike prior work on disentanglement and independent component analysis, we allow for both nontrivial statistical and causal dependencies in the latent space. We study the identifiability of the latent representation based on pairs of views of the observations and prove sufficient conditions that allow us to identify the invariant content partition up to an invertible mapping in both generative and discriminative settings. We find numerical simulations with dependent latent variables are consistent with our theory. Lastly, we introduce Causal3DIdent, a dataset of high-dimensional, visually complex images with rich causal dependencies, which we use to study the effect of data augmentations performed in practice. article_processing_charge: No author: - first_name: Julius von full_name: Kügelgen, Julius von last_name: Kügelgen - first_name: Yash full_name: Sharma, Yash last_name: Sharma - first_name: Luigi full_name: Gresele, Luigi last_name: Gresele - first_name: Wieland full_name: Brendel, Wieland last_name: Brendel - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf - first_name: Michel full_name: Besserve, Michel last_name: Besserve - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Kügelgen J von, Sharma Y, Gresele L, et al. Self-supervised learning with data augmentations provably isolates content from style. In: Advances in Neural Information Processing Systems. Vol 34. ; 2021:16451-16467.' apa: Kügelgen, J. von, Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., & Locatello, F. (2021). Self-supervised learning with data augmentations provably isolates content from style. In Advances in Neural Information Processing Systems (Vol. 34, pp. 16451–16467). Virtual. chicago: Kügelgen, Julius von, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” In Advances in Neural Information Processing Systems, 34:16451–67, 2021. ieee: J. von Kügelgen et al., “Self-supervised learning with data augmentations provably isolates content from style,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 16451–16467. ista: 'Kügelgen J von, Sharma Y, Gresele L, Brendel W, Schölkopf B, Besserve M, Locatello F. 2021. Self-supervised learning with data augmentations provably isolates content from style. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 16451–16467.' mla: Kügelgen, Julius von, et al. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 16451–67. short: J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp. 16451–16467. conference: end_date: 2021-12-10 location: Virtual name: 'NeurIPS: Neural Information Processing Systems' start_date: 2021-12-07 date_created: 2023-08-22T14:04:36Z date_published: 2021-06-08T00:00:00Z date_updated: 2023-09-11T10:33:19Z day: '08' department: - _id: FrLo extern: '1' external_id: arxiv: - '2106.04619' intvolume: ' 34' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2106.04619 month: '06' oa: 1 oa_version: Preprint page: 16451-16467 publication: Advances in Neural Information Processing Systems publication_identifier: isbn: - '9781713845393' publication_status: published quality_controlled: '1' status: public title: Self-supervised learning with data augmentations provably isolates content from style type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 34 year: '2021' ... --- _id: '14180' abstract: - lang: eng text: 'Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning and reuse of knowledge. In this work, we present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules, which we call \emph{functions}. Inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. The proposed architecture can flexibly compose computation along width and depth, and lends itself well to capacity extension after training. To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct settings: image classification and visual abstract reasoning on Raven Progressive Matrices. In the former, we show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner. In the latter, we find that Neural Interpreters are competitive with respect to the state-of-the-art in terms of systematic generalization. ' article_processing_charge: No author: - first_name: Nasim full_name: Rahaman, Nasim last_name: Rahaman - first_name: Muhammad Waleed full_name: Gondal, Muhammad Waleed last_name: Gondal - first_name: Shruti full_name: Joshi, Shruti last_name: Joshi - first_name: Peter full_name: Gehler, Peter last_name: Gehler - first_name: Yoshua full_name: Bengio, Yoshua last_name: Bengio - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Rahaman N, Gondal MW, Joshi S, et al. Dynamic inference with neural interpreters. In: Advances in Neural Information Processing Systems. Vol 34. ; 2021:10985-10998.' apa: Rahaman, N., Gondal, M. W., Joshi, S., Gehler, P., Bengio, Y., Locatello, F., & Schölkopf, B. (2021). Dynamic inference with neural interpreters. In Advances in Neural Information Processing Systems (Vol. 34, pp. 10985–10998). Virtual. chicago: Rahaman, Nasim, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua Bengio, Francesco Locatello, and Bernhard Schölkopf. “Dynamic Inference with Neural Interpreters.” In Advances in Neural Information Processing Systems, 34:10985–98, 2021. ieee: N. Rahaman et al., “Dynamic inference with neural interpreters,” in Advances in Neural Information Processing Systems, Virtual, 2021, vol. 34, pp. 10985–10998. ista: 'Rahaman N, Gondal MW, Joshi S, Gehler P, Bengio Y, Locatello F, Schölkopf B. 2021. Dynamic inference with neural interpreters. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 10985–10998.' mla: Rahaman, Nasim, et al. “Dynamic Inference with Neural Interpreters.” Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 10985–98. short: N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B. Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998. conference: end_date: 2021-12-10 location: Virtual name: 'NeurIPS: Neural Information Processing Systems' start_date: 2021-12-07 date_created: 2023-08-22T14:04:55Z date_published: 2021-10-12T00:00:00Z date_updated: 2023-09-11T11:33:46Z day: '12' department: - _id: FrLo extern: '1' external_id: arxiv: - '2110.06399' intvolume: ' 34' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2110.06399 month: '10' oa: 1 oa_version: Preprint page: 10985-10998 publication: Advances in Neural Information Processing Systems publication_identifier: isbn: - '9781713845393' publication_status: published quality_controlled: '1' status: public title: Dynamic inference with neural interpreters type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 34 year: '2021' ... --- _id: '14117' abstract: - lang: eng text: 'The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.' article_processing_charge: No article_type: original author: - first_name: Bernhard full_name: Scholkopf, Bernhard last_name: Scholkopf - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer - first_name: Nan Rosemary full_name: Ke, Nan Rosemary last_name: Ke - first_name: Nal full_name: Kalchbrenner, Nal last_name: Kalchbrenner - first_name: Anirudh full_name: Goyal, Anirudh last_name: Goyal - first_name: Yoshua full_name: Bengio, Yoshua last_name: Bengio citation: ama: Scholkopf B, Locatello F, Bauer S, et al. Toward causal representation learning. Proceedings of the IEEE. 2021;109(5):612-634. doi:10.1109/jproc.2021.3058954 apa: Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/jproc.2021.3058954 chicago: Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation Learning.” Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2021. https://doi.org/10.1109/jproc.2021.3058954. ieee: B. Scholkopf et al., “Toward causal representation learning,” Proceedings of the IEEE, vol. 109, no. 5. Institute of Electrical and Electronics Engineers, pp. 612–634, 2021. ista: Scholkopf B, Locatello F, Bauer S, Ke NR, Kalchbrenner N, Goyal A, Bengio Y. 2021. Toward causal representation learning. Proceedings of the IEEE. 109(5), 612–634. mla: Scholkopf, Bernhard, et al. “Toward Causal Representation Learning.” Proceedings of the IEEE, vol. 109, no. 5, Institute of Electrical and Electronics Engineers, 2021, pp. 612–34, doi:10.1109/jproc.2021.3058954. short: B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634. date_created: 2023-08-21T12:19:30Z date_published: 2021-05-01T00:00:00Z date_updated: 2023-09-11T11:43:35Z day: '01' department: - _id: FrLo doi: 10.1109/jproc.2021.3058954 extern: '1' external_id: arxiv: - '2102.11107' intvolume: ' 109' issue: '5' keyword: - Electrical and Electronic Engineering language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1109/JPROC.2021.3058954 month: '05' oa: 1 oa_version: Published Version page: 612-634 publication: Proceedings of the IEEE publication_identifier: eissn: - 1558-2256 issn: - 0018-9219 publication_status: published publisher: Institute of Electrical and Electronics Engineers quality_controlled: '1' scopus_import: '1' status: public title: Toward causal representation learning type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 109 year: '2021' ... --- _id: '14178' abstract: - lang: eng text: Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and real-world impact remain questionable. We introduce a new high-resolution dataset with 1M simulated images and over 1,800 annotated real-world images of the same setup. In contrast to previous work, this new dataset exhibits correlations, a complex underlying structure, and allows to evaluate transfer to unseen simulated and real-world settings where the encoder i) remains in distribution or ii) is out of distribution. We propose new architectures in order to scale disentangled representation learning to realistic high-resolution settings and conduct a large-scale empirical study of disentangled representations on this dataset. We observe that disentanglement is a good predictor for out-of-distribution (OOD) task performance. article_processing_charge: No author: - first_name: Andrea full_name: Dittadi, Andrea last_name: Dittadi - first_name: Frederik full_name: Träuble, Frederik last_name: Träuble - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Manuel full_name: Wüthrich, Manuel last_name: Wüthrich - first_name: Vaibhav full_name: Agrawal, Vaibhav last_name: Agrawal - first_name: Ole full_name: Winther, Ole last_name: Winther - first_name: Stefan full_name: Bauer, Stefan last_name: Bauer - first_name: Bernhard full_name: Schölkopf, Bernhard last_name: Schölkopf citation: ama: 'Dittadi A, Träuble F, Locatello F, et al. On the transfer of disentangled representations in realistic settings. In: The Ninth International Conference on Learning Representations. ; 2021.' apa: Dittadi, A., Träuble, F., Locatello, F., Wüthrich, M., Agrawal, V., Winther, O., … Schölkopf, B. (2021). On the transfer of disentangled representations in realistic settings. In The Ninth International Conference on Learning Representations. Virtual. chicago: Dittadi, Andrea, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf. “On the Transfer of Disentangled Representations in Realistic Settings.” In The Ninth International Conference on Learning Representations, 2021. ieee: A. Dittadi et al., “On the transfer of disentangled representations in realistic settings,” in The Ninth International Conference on Learning Representations, Virtual, 2021. ista: 'Dittadi A, Träuble F, Locatello F, Wüthrich M, Agrawal V, Winther O, Bauer S, Schölkopf B. 2021. On the transfer of disentangled representations in realistic settings. The Ninth International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Dittadi, Andrea, et al. “On the Transfer of Disentangled Representations in Realistic Settings.” The Ninth International Conference on Learning Representations, 2021. short: A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther, S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations, 2021. conference: end_date: 2021-05-07 location: Virtual name: 'ICLR: International Conference on Learning Representations' start_date: 2021-05-03 date_created: 2023-08-22T14:04:16Z date_published: 2021-05-04T00:00:00Z date_updated: 2023-09-11T10:55:30Z day: '04' department: - _id: FrLo extern: '1' external_id: arxiv: - '2010.14407' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2010.14407 month: '05' oa: 1 oa_version: Preprint publication: The Ninth International Conference on Learning Representations publication_status: published quality_controlled: '1' status: public title: On the transfer of disentangled representations in realistic settings type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '14221' abstract: - lang: eng text: 'The world is structured in countless ways. It may be prudent to enforce corresponding structural properties to a learning algorithm''s solution, such as incorporating prior beliefs, natural constraints, or causal structures. Doing so may translate to faster, more accurate, and more flexible models, which may directly relate to real-world impact. In this dissertation, we consider two different research areas that concern structuring a learning algorithm''s solution: when the structure is known and when it has to be discovered.' article_number: '2111.13693' article_processing_charge: No author: - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: Locatello F. Enforcing and discovering structure in machine learning. arXiv. doi:10.48550/arXiv.2111.13693 apa: Locatello, F. (n.d.). Enforcing and discovering structure in machine learning. arXiv. https://doi.org/10.48550/arXiv.2111.13693 chicago: Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2111.13693. ieee: F. Locatello, “Enforcing and discovering structure in machine learning,” arXiv. . ista: Locatello F. Enforcing and discovering structure in machine learning. arXiv, 2111.13693. mla: Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.” ArXiv, 2111.13693, doi:10.48550/arXiv.2111.13693. short: F. Locatello, ArXiv (n.d.). date_created: 2023-08-22T14:23:35Z date_published: 2021-11-26T00:00:00Z date_updated: 2023-09-12T07:04:44Z day: '26' department: - _id: FrLo doi: 10.48550/arXiv.2111.13693 extern: '1' external_id: arxiv: - '2111.13693' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2111.13693 month: '11' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Enforcing and discovering structure in machine learning type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '14278' abstract: - lang: eng text: 'The Birkhoff conjecture says that the boundary of a strictly convex integrable billiard table is necessarily an ellipse. In this article, we consider a stronger notion of integrability, namely, integrability close to the boundary, and prove a local version of this conjecture: a small perturbation of almost every ellipse that preserves integrability near the boundary, is itself an ellipse. We apply this result to study local spectral rigidity of ellipses using the connection between the wave trace of the Laplacian and the dynamics near the boundary and establish rigidity for almost all of them.' article_number: '2111.12171' article_processing_charge: No author: - first_name: Illya full_name: Koval, Illya id: 2eed1f3b-896a-11ed-bdf8-93c7c4bf159e last_name: Koval citation: ama: Koval I. Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse. arXiv. doi:10.48550/ARXIV.2111.12171 apa: Koval, I. (n.d.). Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse. arXiv. https://doi.org/10.48550/ARXIV.2111.12171 chicago: Koval, Illya. “Local Strong Birkhoff Conjecture and Local Spectral Rigidity of Almost Every Ellipse.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2111.12171. ieee: I. Koval, “Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse,” arXiv. . ista: Koval I. Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse. arXiv, 2111.12171. mla: Koval, Illya. “Local Strong Birkhoff Conjecture and Local Spectral Rigidity of Almost Every Ellipse.” ArXiv, 2111.12171, doi:10.48550/ARXIV.2111.12171. short: I. Koval, ArXiv (n.d.). date_created: 2023-09-06T08:35:43Z date_published: 2021-11-23T00:00:00Z date_updated: 2023-09-15T06:44:00Z day: '23' department: - _id: GradSch doi: 10.48550/ARXIV.2111.12171 external_id: arxiv: - '2111.12171' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2111.12171 month: '11' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Local strong Birkhoff conjecture and local spectral rigidity of almost every ellipse type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '10199' abstract: - lang: eng text: The design and verification of concurrent systems remains an open challenge due to the non-determinism that arises from the inter-process communication. In particular, concurrent programs are notoriously difficult both to be written correctly and to be analyzed formally, as complex thread interaction has to be accounted for. The difficulties are further exacerbated when concurrent programs get executed on modern-day hardware, which contains various buffering and caching mechanisms for efficiency reasons. This causes further subtle non-determinism, which can often produce very unintuitive behavior of the concurrent programs. Model checking is at the forefront of tackling the verification problem, where the task is to decide, given as input a concurrent system and a desired property, whether the system satisfies the property. The inherent state-space explosion problem in model checking of concurrent systems causes naïve explicit methods not to scale, thus more inventive methods are required. One such method is stateless model checking (SMC), which explores in memory-efficient manner the program executions rather than the states of the program. State-of-the-art SMC is typically coupled with partial order reduction (POR) techniques, which argue that certain executions provably produce identical system behavior, thus limiting the amount of executions one needs to explore in order to cover all possible behaviors. Another method to tackle the state-space explosion is symbolic model checking, where the considered techniques operate on a succinct implicit representation of the input system rather than explicitly accessing the system. In this thesis we present new techniques for verification of concurrent systems. We present several novel POR methods for SMC of concurrent programs under various models of semantics, some of which account for write-buffering mechanisms. Additionally, we present novel algorithms for symbolic model checking of finite-state concurrent systems, where the desired property of the systems is to ensure a formally defined notion of fairness. acknowledged_ssus: - _id: SSU alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Viktor full_name: Toman, Viktor id: 3AF3DA7C-F248-11E8-B48F-1D18A9856A87 last_name: Toman orcid: 0000-0001-9036-063X citation: ama: Toman V. Improved verification techniques for concurrent systems. 2021. doi:10.15479/at:ista:10199 apa: Toman, V. (2021). Improved verification techniques for concurrent systems. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10199 chicago: Toman, Viktor. “Improved Verification Techniques for Concurrent Systems.” Institute of Science and Technology Austria, 2021. https://doi.org/10.15479/at:ista:10199. ieee: V. Toman, “Improved verification techniques for concurrent systems,” Institute of Science and Technology Austria, 2021. ista: Toman V. 2021. Improved verification techniques for concurrent systems. Institute of Science and Technology Austria. mla: Toman, Viktor. Improved Verification Techniques for Concurrent Systems. Institute of Science and Technology Austria, 2021, doi:10.15479/at:ista:10199. short: V. Toman, Improved Verification Techniques for Concurrent Systems, Institute of Science and Technology Austria, 2021. date_created: 2021-10-29T20:09:01Z date_published: 2021-10-31T00:00:00Z date_updated: 2023-09-19T09:59:54Z day: '31' ddc: - '000' degree_awarded: PhD department: - _id: GradSch - _id: KrCh doi: 10.15479/at:ista:10199 ec_funded: 1 file: - access_level: open_access checksum: 4f412a1ee60952221b499a4b1268df35 content_type: application/pdf creator: vtoman date_created: 2021-11-08T14:12:22Z date_updated: 2021-11-08T14:12:22Z file_id: '10225' file_name: toman_th_final.pdf file_size: 2915234 relation: main_file - access_level: closed checksum: 9584943f99127be2dd2963f6784c37d4 content_type: application/zip creator: vtoman date_created: 2021-11-08T14:12:46Z date_updated: 2021-11-09T09:00:50Z file_id: '10226' file_name: toman_thesis.zip file_size: 8616056 relation: source_file file_date_updated: 2021-11-09T09:00:50Z has_accepted_license: '1' keyword: - concurrency - verification - model checking language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: '166' project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program - _id: 25F2ACDE-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: S11402-N23 name: Rigorous Systems Engineering - _id: 25892FC0-B435-11E9-9278-68D0E5697425 grant_number: ICT15-003 name: Efficient Algorithms for Computer Aided Verification - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '10190' relation: part_of_dissertation status: public - id: '10191' relation: part_of_dissertation status: public - id: '9987' relation: part_of_dissertation status: public - id: '141' relation: part_of_dissertation status: public status: public supervisor: - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X title: Improved verification techniques for concurrent systems type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2021' ... --- _id: '8429' abstract: - lang: eng text: We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only ≤10% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32–44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having ≥95% probability of contributing ≥0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data. acknowledgement: This project was funded by an SNSF Eccellenza Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science and Technology Austria. We would like to thank the participants of the cohort studies, and the Ecole Polytechnique Federal Lausanne (EPFL) SCITAS for their excellent compute resources, their generosity with their time and the kindness of their support. P.M.V. acknowledges funding from the Australian National Health and Medical Research Council (1113400) and the Australian Research Council (FL180100072). L.R. acknowledges funding from the Kjell & Märta Beijer Foundation (Stockholm, Sweden). We also would like to acknowledge Simone Rubinacci, Oliver Delanau, Alexander Terenin, Eleonora Porcu, and Mike Goddard for their useful comments and suggestions. article_number: '6972' article_processing_charge: No article_type: original author: - first_name: Marion full_name: Patxot, Marion last_name: Patxot - first_name: Daniel full_name: Trejo Banos, Daniel last_name: Trejo Banos - first_name: Athanasios full_name: Kousathanas, Athanasios last_name: Kousathanas - first_name: Etienne J full_name: Orliac, Etienne J last_name: Orliac - first_name: Sven E full_name: Ojavee, Sven E last_name: Ojavee - first_name: Gerhard full_name: Moser, Gerhard last_name: Moser - first_name: Julia full_name: Sidorenko, Julia last_name: Sidorenko - first_name: Zoltan full_name: Kutalik, Zoltan last_name: Kutalik - first_name: Reedik full_name: Magi, Reedik last_name: Magi - first_name: Peter M full_name: Visscher, Peter M last_name: Visscher - first_name: Lars full_name: Ronnegard, Lars last_name: Ronnegard - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Patxot M, Trejo Banos D, Kousathanas A, et al. Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. Nature Communications. 2021;12(1). doi:10.1038/s41467-021-27258-9 apa: Patxot, M., Trejo Banos, D., Kousathanas, A., Orliac, E. J., Ojavee, S. E., Moser, G., … Robinson, M. R. (2021). Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-021-27258-9 chicago: Patxot, Marion, Daniel Trejo Banos, Athanasios Kousathanas, Etienne J Orliac, Sven E Ojavee, Gerhard Moser, Julia Sidorenko, et al. “Probabilistic Inference of the Genetic Architecture Underlying Functional Enrichment of Complex Traits.” Nature Communications. Springer Nature, 2021. https://doi.org/10.1038/s41467-021-27258-9. ieee: M. Patxot et al., “Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits,” Nature Communications, vol. 12, no. 1. Springer Nature, 2021. ista: Patxot M, Trejo Banos D, Kousathanas A, Orliac EJ, Ojavee SE, Moser G, Sidorenko J, Kutalik Z, Magi R, Visscher PM, Ronnegard L, Robinson MR. 2021. Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits. Nature Communications. 12(1), 6972. mla: Patxot, Marion, et al. “Probabilistic Inference of the Genetic Architecture Underlying Functional Enrichment of Complex Traits.” Nature Communications, vol. 12, no. 1, 6972, Springer Nature, 2021, doi:10.1038/s41467-021-27258-9. short: M. Patxot, D. Trejo Banos, A. Kousathanas, E.J. Orliac, S.E. Ojavee, G. Moser, J. Sidorenko, Z. Kutalik, R. Magi, P.M. Visscher, L. Ronnegard, M.R. Robinson, Nature Communications 12 (2021). date_created: 2020-09-17T10:52:38Z date_published: 2021-11-30T00:00:00Z date_updated: 2023-09-26T10:36:14Z day: '30' ddc: - '610' department: - _id: MaRo doi: 10.1038/s41467-021-27258-9 external_id: isi: - '000724450600023' file: - access_level: open_access checksum: 384681be17aff902c149a48f52d13d4f content_type: application/pdf creator: cchlebak date_created: 2021-12-06T07:47:11Z date_updated: 2021-12-06T07:47:11Z file_id: '10419' file_name: 2021_NatComm_Paxtot.pdf file_size: 6519771 relation: main_file success: 1 file_date_updated: 2021-12-06T07:47:11Z has_accepted_license: '1' intvolume: ' 12' isi: 1 issue: '1' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '11' oa: 1 oa_version: Published Version publication: Nature Communications publication_identifier: eissn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '13063' relation: research_data status: public scopus_import: '1' status: public title: Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 12 year: '2021' ... --- _id: '10854' abstract: - lang: eng text: "Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner?\r\nTo address this question, we define the batch dynamic CONGEST model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labelled graph under batch changes. We investigate, when a batch of alpha edge label changes arrive, - how much time as a function of alpha we need to update an existing solution, and - how much information the nodes have to keep in local memory between batches in order to update the solution quickly.\r\nOur work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. The diverse time complexity of our model spans from constant time, through time polynomial in alpha, and to alpha time, which we show to be enough for any task." acknowledgement: We thank Jukka Suomela for discussions. We also thank our shepherd Mohammad Hajiesmaili and the reviewers for their time and suggestions on how to improve the paper. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML), from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska–Curie grant agreement No. 840605, from the Vienna Science and Technology Fund (WWTF) project WHATIF, ICT19-045, 2020-2024, and from the Austrian Science Fund (FWF) and netIDEE SCIENCE project P 33775-N. article_processing_charge: No author: - first_name: Klaus-Tycho full_name: Foerster, Klaus-Tycho last_name: Foerster - first_name: Janne full_name: Korhonen, Janne id: C5402D42-15BC-11E9-A202-CA2BE6697425 last_name: Korhonen - first_name: Ami full_name: Paz, Ami last_name: Paz - first_name: Joel full_name: Rybicki, Joel id: 334EFD2E-F248-11E8-B48F-1D18A9856A87 last_name: Rybicki orcid: 0000-0002-6432-6646 - first_name: Stefan full_name: Schmid, Stefan last_name: Schmid citation: ama: 'Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. Input-dynamic distributed algorithms for communication networks. In: Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. Association for Computing Machinery; 2021:71-72. doi:10.1145/3410220.3453923' apa: 'Foerster, K.-T., Korhonen, J., Paz, A., Rybicki, J., & Schmid, S. (2021). Input-dynamic distributed algorithms for communication networks. In Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems (pp. 71–72). Virtual, Online: Association for Computing Machinery. https://doi.org/10.1145/3410220.3453923' chicago: Foerster, Klaus-Tycho, Janne Korhonen, Ami Paz, Joel Rybicki, and Stefan Schmid. “Input-Dynamic Distributed Algorithms for Communication Networks.” In Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, 71–72. Association for Computing Machinery, 2021. https://doi.org/10.1145/3410220.3453923. ieee: K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic distributed algorithms for communication networks,” in Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Virtual, Online, 2021, pp. 71–72. ista: 'Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. 2021. Input-dynamic distributed algorithms for communication networks. Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. SIGMETRICS: International Conference on Measurement and Modeling of Computer Systems, 71–72.' mla: Foerster, Klaus-Tycho, et al. “Input-Dynamic Distributed Algorithms for Communication Networks.” Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Association for Computing Machinery, 2021, pp. 71–72, doi:10.1145/3410220.3453923. short: K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, S. Schmid, in:, Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Association for Computing Machinery, 2021, pp. 71–72. conference: end_date: 2021-06-18 location: Virtual, Online name: 'SIGMETRICS: International Conference on Measurement and Modeling of Computer Systems' start_date: 2021-06-14 date_created: 2022-03-18T08:48:41Z date_published: 2021-05-01T00:00:00Z date_updated: 2023-09-26T10:40:55Z day: '01' department: - _id: DaAl doi: 10.1145/3410220.3453923 ec_funded: 1 external_id: arxiv: - '2005.07637' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2005.07637 month: '05' oa: 1 oa_version: Preprint page: 71-72 project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning - _id: 26A5D39A-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '840605' name: Coordination in constrained and natural distributed systems publication: Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems publication_identifier: isbn: - '9781450380720' publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' related_material: record: - id: '10855' relation: extended_version status: public scopus_import: '1' status: public title: Input-dynamic distributed algorithms for communication networks type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '10855' abstract: - lang: eng text: 'Consider a distributed task where the communication network is fixed but the local inputs given to the nodes of the distributed system may change over time. In this work, we explore the following question: if some of the local inputs change, can an existing solution be updated efficiently, in a dynamic and distributed manner? To address this question, we define the batch dynamic \congest model in which we are given a bandwidth-limited communication network and a dynamic edge labelling defines the problem input. The task is to maintain a solution to a graph problem on the labeled graph under batch changes. We investigate, when a batch of α edge label changes arrive, \beginitemize \item how much time as a function of α we need to update an existing solution, and \item how much information the nodes have to keep in local memory between batches in order to update the solution quickly. \enditemize Our work lays the foundations for the theory of input-dynamic distributed network algorithms. We give a general picture of the complexity landscape in this model, design both universal algorithms and algorithms for concrete problems, and present a general framework for lower bounds. In particular, we derive non-trivial upper bounds for two selected, contrasting problems: maintaining a minimum spanning tree and detecting cliques.' acknowledgement: "We thank Jukka Suomela for discussions. We also thank our shepherd Mohammad Hajiesmaili\r\nand the reviewers for their time and suggestions on how to improve the paper. This project\r\nhas received funding from the European Research Council (ERC) under the European Union’s\r\nHorizon 2020 research and innovation programme (grant agreement No 805223 ScaleML), from the European Union’s Horizon 2020 research and innovation programme under the Marie\r\nSk lodowska–Curie grant agreement No. 840605, from the Vienna Science and Technology Fund (WWTF) project WHATIF, ICT19-045, 2020-2024, and from the Austrian Science Fund (FWF) and netIDEE SCIENCE project P 33775-N." article_processing_charge: No article_type: original author: - first_name: Klaus-Tycho full_name: Foerster, Klaus-Tycho last_name: Foerster - first_name: Janne full_name: Korhonen, Janne id: C5402D42-15BC-11E9-A202-CA2BE6697425 last_name: Korhonen - first_name: Ami full_name: Paz, Ami last_name: Paz - first_name: Joel full_name: Rybicki, Joel id: 334EFD2E-F248-11E8-B48F-1D18A9856A87 last_name: Rybicki orcid: 0000-0002-6432-6646 - first_name: Stefan full_name: Schmid, Stefan last_name: Schmid citation: ama: Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. Input-dynamic distributed algorithms for communication networks. Proceedings of the ACM on Measurement and Analysis of Computing Systems. 2021;5(1):1-33. doi:10.1145/3447384 apa: Foerster, K.-T., Korhonen, J., Paz, A., Rybicki, J., & Schmid, S. (2021). Input-dynamic distributed algorithms for communication networks. Proceedings of the ACM on Measurement and Analysis of Computing Systems. Association for Computing Machinery. https://doi.org/10.1145/3447384 chicago: Foerster, Klaus-Tycho, Janne Korhonen, Ami Paz, Joel Rybicki, and Stefan Schmid. “Input-Dynamic Distributed Algorithms for Communication Networks.” Proceedings of the ACM on Measurement and Analysis of Computing Systems. Association for Computing Machinery, 2021. https://doi.org/10.1145/3447384. ieee: K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic distributed algorithms for communication networks,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 5, no. 1. Association for Computing Machinery, pp. 1–33, 2021. ista: Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. 2021. Input-dynamic distributed algorithms for communication networks. Proceedings of the ACM on Measurement and Analysis of Computing Systems. 5(1), 1–33. mla: Foerster, Klaus-Tycho, et al. “Input-Dynamic Distributed Algorithms for Communication Networks.” Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 5, no. 1, Association for Computing Machinery, 2021, pp. 1–33, doi:10.1145/3447384. short: K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, S. Schmid, Proceedings of the ACM on Measurement and Analysis of Computing Systems 5 (2021) 1–33. date_created: 2022-03-18T09:10:27Z date_published: 2021-03-01T00:00:00Z date_updated: 2023-09-26T10:40:55Z day: '01' department: - _id: DaAl doi: 10.1145/3447384 ec_funded: 1 external_id: arxiv: - '2005.07637' intvolume: ' 5' issue: '1' keyword: - Computer Networks and Communications - Hardware and Architecture - Safety - Risk - Reliability and Quality - Computer Science (miscellaneous) language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2005.07637 month: '03' oa: 1 oa_version: Preprint page: 1-33 project: - _id: 26A5D39A-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '840605' name: Coordination in constrained and natural distributed systems - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication: Proceedings of the ACM on Measurement and Analysis of Computing Systems publication_identifier: issn: - 2476-1249 publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' related_material: record: - id: '10854' relation: shorter_version status: public scopus_import: '1' status: public title: Input-dynamic distributed algorithms for communication networks type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 5 year: '2021' ... --- _id: '9293' abstract: - lang: eng text: 'We consider planning problems for graphs, Markov Decision Processes (MDPs), and games on graphs in an explicit state space. While graphs represent the most basic planning model, MDPs represent interaction with nature and games on graphs represent interaction with an adversarial environment. We consider two planning problems with k different target sets: (a) the coverage problem asks whether there is a plan for each individual target set; and (b) the sequential target reachability problem asks whether the targets can be reached in a given sequence. For the coverage problem, we present a linear-time algorithm for graphs, and quadratic conditional lower bound for MDPs and games on graphs. For the sequential target problem, we present a linear-time algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic conditional lower bound for games on graphs. Our results with conditional lower bounds, based on the boolean matrix multiplication (BMM) conjecture and strong exponential time hypothesis (SETH), establish (i) model-separation results showing that for the coverage problem MDPs and games on graphs are harder than graphs, and for the sequential reachability problem games on graphs are harder than MDPs and graphs; and (ii) problem-separation results showing that for MDPs the coverage problem is harder than the sequential target problem.' article_number: '103499' article_processing_charge: No article_type: original author: - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Wolfgang full_name: Dvořák, Wolfgang last_name: Dvořák - first_name: Monika H full_name: Henzinger, Monika H id: 540c9bbd-f2de-11ec-812d-d04a5be85630 last_name: Henzinger orcid: 0000-0002-5008-6530 - first_name: Alexander full_name: Svozil, Alexander last_name: Svozil citation: ama: Chatterjee K, Dvořák W, Henzinger MH, Svozil A. Algorithms and conditional lower bounds for planning problems. Artificial Intelligence. 2021;297(8). doi:10.1016/j.artint.2021.103499 apa: Chatterjee, K., Dvořák, W., Henzinger, M. H., & Svozil, A. (2021). Algorithms and conditional lower bounds for planning problems. Artificial Intelligence. Elsevier. https://doi.org/10.1016/j.artint.2021.103499 chicago: Chatterjee, Krishnendu, Wolfgang Dvořák, Monika H Henzinger, and Alexander Svozil. “Algorithms and Conditional Lower Bounds for Planning Problems.” Artificial Intelligence. Elsevier, 2021. https://doi.org/10.1016/j.artint.2021.103499. ieee: K. Chatterjee, W. Dvořák, M. H. Henzinger, and A. Svozil, “Algorithms and conditional lower bounds for planning problems,” Artificial Intelligence, vol. 297, no. 8. Elsevier, 2021. ista: Chatterjee K, Dvořák W, Henzinger MH, Svozil A. 2021. Algorithms and conditional lower bounds for planning problems. Artificial Intelligence. 297(8), 103499. mla: Chatterjee, Krishnendu, et al. “Algorithms and Conditional Lower Bounds for Planning Problems.” Artificial Intelligence, vol. 297, no. 8, 103499, Elsevier, 2021, doi:10.1016/j.artint.2021.103499. short: K. Chatterjee, W. Dvořák, M.H. Henzinger, A. Svozil, Artificial Intelligence 297 (2021). date_created: 2021-03-28T22:01:40Z date_published: 2021-03-16T00:00:00Z date_updated: 2023-09-26T10:41:42Z day: '16' department: - _id: KrCh doi: 10.1016/j.artint.2021.103499 external_id: arxiv: - '1804.07031' isi: - '000657537500003' intvolume: ' 297' isi: 1 issue: '8' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1804.07031 month: '03' oa: 1 oa_version: Preprint publication: Artificial Intelligence publication_identifier: issn: - 0004-3702 publication_status: published publisher: Elsevier quality_controlled: '1' related_material: record: - id: '35' relation: earlier_version status: public scopus_import: '1' status: public title: Algorithms and conditional lower bounds for planning problems type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 297 year: '2021' ... --- _id: '13063' abstract: - lang: eng text: We develop a Bayesian model (BayesRR-RC) that provides robust SNP-heritability estimation, an alternative to marker discovery, and accurate genomic prediction, taking 22 seconds per iteration to estimate 8.4 million SNP-effects and 78 SNP-heritability parameters in the UK Biobank. We find that only $\leq$ 10\% of the genetic variation captured for height, body mass index, cardiovascular disease, and type 2 diabetes is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to introns, and 22-28% to distal 10-500kb upstream regions. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having >95% probability of contributing >0.001% to the genetic variance of these four traits. Our open-source software (GMRM) provides a scalable alternative to current approaches for biobank data. article_processing_charge: No author: - first_name: Matthew Richard full_name: Robinson, Matthew Richard id: E5D42276-F5DA-11E9-8E24-6303E6697425 last_name: Robinson orcid: 0000-0001-8982-8813 citation: ama: Robinson MR. Probabilistic inference of the genetic architecture of functional enrichment of complex traits. 2021. doi:10.5061/dryad.sqv9s4n51 apa: Robinson, M. R. (2021). Probabilistic inference of the genetic architecture of functional enrichment of complex traits. Dryad. https://doi.org/10.5061/dryad.sqv9s4n51 chicago: Robinson, Matthew Richard. “Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits.” Dryad, 2021. https://doi.org/10.5061/dryad.sqv9s4n51. ieee: M. R. Robinson, “Probabilistic inference of the genetic architecture of functional enrichment of complex traits.” Dryad, 2021. ista: Robinson MR. 2021. Probabilistic inference of the genetic architecture of functional enrichment of complex traits, Dryad, 10.5061/dryad.sqv9s4n51. mla: Robinson, Matthew Richard. Probabilistic Inference of the Genetic Architecture of Functional Enrichment of Complex Traits. Dryad, 2021, doi:10.5061/dryad.sqv9s4n51. short: M.R. Robinson, (2021). date_created: 2023-05-23T16:20:16Z date_published: 2021-11-04T00:00:00Z date_updated: 2023-09-26T10:36:15Z day: '04' ddc: - '570' department: - _id: MaRo doi: 10.5061/dryad.sqv9s4n51 license: https://creativecommons.org/publicdomain/zero/1.0/ main_file_link: - open_access: '1' url: https://doi.org/10.5061/dryad.sqv9s4n51 month: '11' oa: 1 oa_version: Published Version publisher: Dryad related_material: link: - relation: software url: https://github.com/medical-genomics-group/gmrm record: - id: '8429' relation: used_in_publication status: public status: public title: Probabilistic inference of the genetic architecture of functional enrichment of complex traits tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '9304' abstract: - lang: eng text: The high processing cost, poor mechanical properties and moderate performance of Bi2Te3–based alloys used in thermoelectric devices limit the cost-effectiveness of this energy conversion technology. Towards solving these current challenges, in the present work, we detail a low temperature solution-based approach to produce Bi2Te3-Cu2-xTe nanocomposites with improved thermoelectric performance. Our approach consists in combining proper ratios of colloidal nanoparticles and to consolidate the resulting mixture into nanocomposites using a hot press. The transport properties of the nanocomposites are characterized and compared with those of pure Bi2Te3 nanomaterials obtained following the same procedure. In contrast with most previous works, the presence of Cu2-xTe nanodomains does not result in a significant reduction of the lattice thermal conductivity of the reference Bi2Te3 nanomaterial, which is already very low. However, the introduction of Cu2-xTe yields a nearly threefold increase of the power factor associated to a simultaneous increase of the Seebeck coefficient and electrical conductivity at temperatures above 400 K. Taking into account the band alignment of the two materials, we rationalize this increase by considering that Cu2-xTe nanostructures, with a relatively low electron affinity, are able to inject electrons into Bi2Te3, enhancing in this way its electrical conductivity. The simultaneous increase of the Seebeck coefficient is related to the energy filtering of charge carriers at energy barriers within Bi2Te3 domains associated with the accumulation of electrons in regions nearby a Cu2-xTe/Bi2Te3 heterojunction. Overall, with the incorporation of a proper amount of Cu2-xTe nanoparticles, we demonstrate a 250% improvement of the thermoelectric figure of merit of Bi2Te3. acknowledgement: "This work was supported by the European Regional Development Funds and by the Generalitat de Catalunya through the project 2017SGR1246. Y.Z, C.X, M.L, K.X and X.H thank the China Scholarship Council for the scholarship support. MI acknowledges financial support from IST Austria. YL acknowledges funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 754411. ICN2\r\nacknowledges funding from Generalitat de Catalunya 2017 SGR 327 and the Spanish MINECO project ENE2017-85087-C3. ICN2 is supported by the Severo Ochoa program from the Spanish MINECO (grant no. SEV-2017-0706) and is funded by the CERCA Program/Generalitat de Catalunya. Part of the present work has been performed in the framework of Universitat Autònoma de Barcelona Materials Science PhD program." article_number: '129374' article_processing_charge: No article_type: original author: - first_name: Yu full_name: Zhang, Yu last_name: Zhang - first_name: Congcong full_name: Xing, Congcong last_name: Xing - first_name: Yu full_name: Liu, Yu id: 2A70014E-F248-11E8-B48F-1D18A9856A87 last_name: Liu orcid: 0000-0001-7313-6740 - first_name: Mengyao full_name: Li, Mengyao last_name: Li - first_name: Ke full_name: Xiao, Ke last_name: Xiao - first_name: Pablo full_name: Guardia, Pablo last_name: Guardia - first_name: Seungho full_name: Lee, Seungho id: BB243B88-D767-11E9-B658-BC13E6697425 last_name: Lee orcid: 0000-0002-6962-8598 - first_name: Xu full_name: Han, Xu last_name: Han - first_name: Ahmad full_name: Moghaddam, Ahmad last_name: Moghaddam - first_name: Joan J full_name: Roa, Joan J last_name: Roa - first_name: Jordi full_name: Arbiol, Jordi last_name: Arbiol - first_name: Maria full_name: Ibáñez, Maria id: 43C61214-F248-11E8-B48F-1D18A9856A87 last_name: Ibáñez orcid: 0000-0001-5013-2843 - first_name: Kai full_name: Pan, Kai last_name: Pan - first_name: Mirko full_name: Prato, Mirko last_name: Prato - first_name: Ying full_name: Xie, Ying last_name: Xie - first_name: Andreu full_name: Cabot, Andreu last_name: Cabot citation: ama: Zhang Y, Xing C, Liu Y, et al. Influence of copper telluride nanodomains on the transport properties of n-type bismuth telluride. Chemical Engineering Journal. 2021;418(8). doi:10.1016/j.cej.2021.129374 apa: Zhang, Y., Xing, C., Liu, Y., Li, M., Xiao, K., Guardia, P., … Cabot, A. (2021). Influence of copper telluride nanodomains on the transport properties of n-type bismuth telluride. Chemical Engineering Journal. Elsevier. https://doi.org/10.1016/j.cej.2021.129374 chicago: Zhang, Yu, Congcong Xing, Yu Liu, Mengyao Li, Ke Xiao, Pablo Guardia, Seungho Lee, et al. “Influence of Copper Telluride Nanodomains on the Transport Properties of N-Type Bismuth Telluride.” Chemical Engineering Journal. Elsevier, 2021. https://doi.org/10.1016/j.cej.2021.129374. ieee: Y. Zhang et al., “Influence of copper telluride nanodomains on the transport properties of n-type bismuth telluride,” Chemical Engineering Journal, vol. 418, no. 8. Elsevier, 2021. ista: Zhang Y, Xing C, Liu Y, Li M, Xiao K, Guardia P, Lee S, Han X, Moghaddam A, Roa JJ, Arbiol J, Ibáñez M, Pan K, Prato M, Xie Y, Cabot A. 2021. Influence of copper telluride nanodomains on the transport properties of n-type bismuth telluride. Chemical Engineering Journal. 418(8), 129374. mla: Zhang, Yu, et al. “Influence of Copper Telluride Nanodomains on the Transport Properties of N-Type Bismuth Telluride.” Chemical Engineering Journal, vol. 418, no. 8, 129374, Elsevier, 2021, doi:10.1016/j.cej.2021.129374. short: Y. Zhang, C. Xing, Y. Liu, M. Li, K. Xiao, P. Guardia, S. Lee, X. Han, A. Moghaddam, J.J. Roa, J. Arbiol, M. Ibáñez, K. Pan, M. Prato, Y. Xie, A. Cabot, Chemical Engineering Journal 418 (2021). date_created: 2021-04-04T22:01:20Z date_published: 2021-08-15T00:00:00Z date_updated: 2023-09-27T07:36:29Z day: '15' department: - _id: MaIb doi: 10.1016/j.cej.2021.129374 ec_funded: 1 external_id: isi: - '000655672000005' intvolume: ' 418' isi: 1 issue: '8' language: - iso: eng main_file_link: - open_access: '1' url: https://ddd.uab.cat/record/271949 month: '08' oa: 1 oa_version: Submitted Version project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Chemical Engineering Journal publication_identifier: issn: - 1385-8947 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: Influence of copper telluride nanodomains on the transport properties of n-type bismuth telluride type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 418 year: '2021' ... --- _id: '9793' abstract: - lang: eng text: Astrocytes extensively infiltrate the neuropil to regulate critical aspects of synaptic development and function. This process is regulated by transcellular interactions between astrocytes and neurons via cell adhesion molecules. How astrocytes coordinate developmental processes among one another to parse out the synaptic neuropil and form non-overlapping territories is unknown. Here we identify a molecular mechanism regulating astrocyte-astrocyte interactions during development to coordinate astrocyte morphogenesis and gap junction coupling. We show that hepaCAM, a disease-linked, astrocyte-enriched cell adhesion molecule, regulates astrocyte competition for territory and morphological complexity in the developing mouse cortex. Furthermore, conditional deletion of Hepacam from developing astrocytes significantly impairs gap junction coupling between astrocytes and disrupts the balance between synaptic excitation and inhibition. Mutations in HEPACAM cause megalencephalic leukoencephalopathy with subcortical cysts in humans. Therefore, our findings suggest that disruption of astrocyte self-organization mechanisms could be an underlying cause of neural pathology. acknowledgement: This work was supported by the National Institutes of Health (R01 DA047258 and R01 NS102237 to C.E., F32 NS100392 to K.T.B.) and the Holland-Trice Brain Research Award (to C.E.). K.T.B. was supported by postdoctoral fellowships from the Foerster-Bernstein Family and The Hartwell Foundation. The Hippenmeyer lab was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovations program (725780 LinPro) to S.H. R.E. was supported by Ministerio de Ciencia y Tecnología (RTI2018-093493-B-I00). We thank the Duke Light Microscopy Core Facility, the Duke Transgenic Mouse Facility, Dr. U. Schulte for assistance with proteomic experiments, and Dr. D. Silver for critical review of the manuscript. Cartoon elements of figure panels were created using BioRender.com. article_processing_charge: No article_type: original author: - first_name: Katherine T. full_name: Baldwin, Katherine T. last_name: Baldwin - first_name: Christabel X. full_name: Tan, Christabel X. last_name: Tan - first_name: Samuel T. full_name: Strader, Samuel T. last_name: Strader - first_name: Changyu full_name: Jiang, Changyu last_name: Jiang - first_name: Justin T. full_name: Savage, Justin T. last_name: Savage - first_name: Xabier full_name: Elorza-Vidal, Xabier last_name: Elorza-Vidal - first_name: Ximena full_name: Contreras, Ximena id: 475990FE-F248-11E8-B48F-1D18A9856A87 last_name: Contreras - first_name: Thomas full_name: Rülicke, Thomas last_name: Rülicke - first_name: Simon full_name: Hippenmeyer, Simon id: 37B36620-F248-11E8-B48F-1D18A9856A87 last_name: Hippenmeyer orcid: 0000-0003-2279-1061 - first_name: Raúl full_name: Estévez, Raúl last_name: Estévez - first_name: Ru-Rong full_name: Ji, Ru-Rong last_name: Ji - first_name: Cagla full_name: Eroglu, Cagla last_name: Eroglu citation: ama: Baldwin KT, Tan CX, Strader ST, et al. HepaCAM controls astrocyte self-organization and coupling. Neuron. 2021;109(15):2427-2442.e10. doi:10.1016/j.neuron.2021.05.025 apa: Baldwin, K. T., Tan, C. X., Strader, S. T., Jiang, C., Savage, J. T., Elorza-Vidal, X., … Eroglu, C. (2021). HepaCAM controls astrocyte self-organization and coupling. Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2021.05.025 chicago: Baldwin, Katherine T., Christabel X. Tan, Samuel T. Strader, Changyu Jiang, Justin T. Savage, Xabier Elorza-Vidal, Ximena Contreras, et al. “HepaCAM Controls Astrocyte Self-Organization and Coupling.” Neuron. Elsevier, 2021. https://doi.org/10.1016/j.neuron.2021.05.025. ieee: K. T. Baldwin et al., “HepaCAM controls astrocyte self-organization and coupling,” Neuron, vol. 109, no. 15. Elsevier, p. 2427–2442.e10, 2021. ista: Baldwin KT, Tan CX, Strader ST, Jiang C, Savage JT, Elorza-Vidal X, Contreras X, Rülicke T, Hippenmeyer S, Estévez R, Ji R-R, Eroglu C. 2021. HepaCAM controls astrocyte self-organization and coupling. Neuron. 109(15), 2427–2442.e10. mla: Baldwin, Katherine T., et al. “HepaCAM Controls Astrocyte Self-Organization and Coupling.” Neuron, vol. 109, no. 15, Elsevier, 2021, p. 2427–2442.e10, doi:10.1016/j.neuron.2021.05.025. short: K.T. Baldwin, C.X. Tan, S.T. Strader, C. Jiang, J.T. Savage, X. Elorza-Vidal, X. Contreras, T. Rülicke, S. Hippenmeyer, R. Estévez, R.-R. Ji, C. Eroglu, Neuron 109 (2021) 2427–2442.e10. date_created: 2021-08-06T09:08:25Z date_published: 2021-08-04T00:00:00Z date_updated: 2023-09-27T07:46:09Z day: '04' department: - _id: SiHi doi: 10.1016/j.neuron.2021.05.025 ec_funded: 1 external_id: isi: - '000692851900010' pmid: - '34171291' intvolume: ' 109' isi: 1 issue: '15' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1016/j.neuron.2021.05.025 month: '08' oa: 1 oa_version: Published Version page: 2427-2442.e10 pmid: 1 project: - _id: 260018B0-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '725780' name: Principles of Neural Stem Cell Lineage Progression in Cerebral Cortex Development publication: Neuron publication_identifier: eissn: - 1097-4199 issn: - 0896-6273 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: HepaCAM controls astrocyte self-organization and coupling type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 109 year: '2021' ...